相关论文: Constrained Optimal Fuel Consumption of HEVs under…
Hybrid electric vehicles (HEVs) are becoming increasingly popular because they can better combine the working characteristics of internal combustion engines and electric motors. However, the minimum fuel consumption of an HEV for a battery…
Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, have the potential to significantly reduce fuel consumption and travel time in real-world driving conditions. In particular, the Eco-driving problem…
Energy management strategy (EMS) is a key technology for plug-in hybrid electric vehicles (PHEVs). The energy management of certain series-parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete…
This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of combustion engine and electric machine in a hybrid electric vehicle (HEV).…
This paper considers the integrated motion control and energy management problems of the series hybrid electric vehicles (SHEV) with constraints. We propose a multi-objective model predictive control (MOMPC)-based energy management…
This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is…
Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle…
Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks, and reduced road capacity utilization. While previous…
The integration of renewable energy sources (RES) and the convergence of transport electrification, creates a significant challenge for distribution network management e.g. voltage and frequency violations, particularly in rural and remote…
We look into minimizing the hydrogen fuel consumption of hydrogen hybrid trains by optimizing their operation. The powertrain considered is a fuel cell charge-sustaining hybrid. Convex optimization is utilized to compute optimal speed and…
Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging…
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to…
Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown…
Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks…
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader…
This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow.…
We address the problem of optimizing the performance of a dynamic system while satisfying hard safety constraints at all times. Implementing an optimal control solution is limited by the computational cost required to derive it in real…
In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas. Among them, reinforcement learning (RL) has proven to be an effective solution for learning intelligent control strategies. As…
In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the…
In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We…